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Dr. Cagatay Catal
1. Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands;

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0 Cyber Security
0 Smart Systems
0 Software Architecture
0 Software Testing
0 Machine Learning, Deep Learning

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Machine Learning, Deep Learning

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Journal article
Published: 17 February 2020 in IEEE Transactions on Cybernetics
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Many Pareto-based multiobjective evolutionary algorithms require ranking the solutions of the population in each iteration according to the dominance principle, which can become a costly operation particularly in the case of dealing with many-objective optimization problems. In this article, we present a new efficient algorithm for computing the nondominated sorting procedure, called merge nondominated sorting (MNDS), which has a best computational complexity of O(Nłog N) and a worst computational complexity of O(MN²), with N being the population size and M being the number of objectives. Our approach is based on the computation of the dominance set, that is, for each solution, the set of solutions that dominate it, by taking advantage of the characteristics of the merge sort algorithm. We compare MNDS against six well-known techniques that can be considered as the state-of-the-art. The results indicate that the MNDS algorithm outperforms the other techniques in terms of the number of comparisons as well as the total running time.

ACS Style

Javier Moreno; Daniel Rodriguez; Antonio J. Nebro; Jose A. Lozano. Merge Nondominated Sorting Algorithm for Many-Objective Optimization. IEEE Transactions on Cybernetics 2020, 1 -11.

AMA Style

Javier Moreno, Daniel Rodriguez, Antonio J. Nebro, Jose A. Lozano. Merge Nondominated Sorting Algorithm for Many-Objective Optimization. IEEE Transactions on Cybernetics. 2020; (99):1-11.

Chicago/Turabian Style

Javier Moreno; Daniel Rodriguez; Antonio J. Nebro; Jose A. Lozano. 2020. "Merge Nondominated Sorting Algorithm for Many-Objective Optimization." IEEE Transactions on Cybernetics , no. 99: 1-11.

Original article
Published: 21 November 2019 in Virtual Reality
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Building evacuation training systems and training employees in an organization have a vital role in emergency cases in which people need to know what to do exactly. In every building, procedures, rules, and actions are attractively shown on the walls, but most of the people living in that building are not aware of these procedures and do not have any experience what to do in these dangerous situations. In order to be able to apply these procedures properly in an emergency situation, community members should be trained with the state-of-the-art equipment and technologies, but to do so, up-front investment and development of such a system are necessary. In this study, augmented reality (AR) technology was applied to realize a game-based evacuation training system that implements gamification practices. The architectural plans of a university were used to model the floors and the relevant environment. Employees are trained to learn how to reach the nearest exit location in the event of a fire or earthquake, and also, the system provides the shortest path for the evacuation. In addition to these features, our training game has educational animations about the fire, chemical attack, and earthquake events. A mobile application was implemented to train employees working in the building and inform them to know how to escape in an emergency situation. The technology acceptance model and the related questionnaire form were applied, and the response of 36 participants was analyzed. It was demonstrated that AR and relevant tools provide a flexible environment to develop evacuation systems in a university, our mobile application enabled participants to be trained in a realistic environment, and trainees were highly satisfied with the system. Educational animations were also another benefit for the trainees.

ACS Style

Cagatay Catal; Akhan Akbulut; Berkay Tunali; Erol Ulug; Eren Ozturk. Evaluation of augmented reality technology for the design of an evacuation training game. Virtual Reality 2019, 24, 359 -368.

AMA Style

Cagatay Catal, Akhan Akbulut, Berkay Tunali, Erol Ulug, Eren Ozturk. Evaluation of augmented reality technology for the design of an evacuation training game. Virtual Reality. 2019; 24 (3):359-368.

Chicago/Turabian Style

Cagatay Catal; Akhan Akbulut; Berkay Tunali; Erol Ulug; Eren Ozturk. 2019. "Evaluation of augmented reality technology for the design of an evacuation training game." Virtual Reality 24, no. 3: 359-368.

Journal article
Published: 08 August 2019 in IEEE Aerospace and Electronic Systems Magazine
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The design of a Compact Dual-band Equatorial helix antenna using Computational Electromagnetic Methods together with multiobjective optimization algorithms is presented. These antennas are used for Telemetry, Tracking, and Control of satellites from the terrain base station. In order to optimize the parameters an antenna, a simulation-optimization process is shown along a real case study. The parameters of the antenna that fulfills the radiation patterns needed for the communication are obtained using a simulation tool called MONURBS together with two well-known multiobjective algorithms: NSGA-II and SPEA-2. In this paper, a comparison with previous designs and the antenna prototype is presented, showing that this approach can obtain multiple valid solutions and accelerate the design process.

ACS Style

Javier Moreno; Ivan Gonzalez; Daniel Rodriguez. Design of a TTC Antenna Using Simulation and Multiobjective Evolutionary Algorithms. IEEE Aerospace and Electronic Systems Magazine 2019, 34, 18 -31.

AMA Style

Javier Moreno, Ivan Gonzalez, Daniel Rodriguez. Design of a TTC Antenna Using Simulation and Multiobjective Evolutionary Algorithms. IEEE Aerospace and Electronic Systems Magazine. 2019; 34 (7):18-31.

Chicago/Turabian Style

Javier Moreno; Ivan Gonzalez; Daniel Rodriguez. 2019. "Design of a TTC Antenna Using Simulation and Multiobjective Evolutionary Algorithms." IEEE Aerospace and Electronic Systems Magazine 34, no. 7: 18-31.

Review
Published: 12 June 2019 in Journal of Systems and Software
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According to various reports, many software engineering (SE) graduates often face difficulties when beginning their careers, which is mainly due to misalignment of the skills learned in university education with what is needed in the software industry. Our objective is to perform a meta-analysis to aggregate the results of the studies published in this area to provide a consolidated view on how to align SE education with industry needs, to identify the most important skills and also existing knowledge gaps. To synthesize the body of knowledge, we performed a systematic literature review (SLR), in which we systematically selected a pool of 35 studies and then conducted a meta-analysis using data extracted from those studies. Via a meta-analysis and using data from 13 countries and over 4,000 data points, highlights of the SLR include: (1) software requirements, design, and testing are the most important skills; and (2) the greatest knowledge gaps are in configuration management, SE models and methods, SE process, design (and architecture), as well as in testing. This paper provides implications for both educators and hiring managers by listing the most important SE skills and the knowledge gaps in the industry.

ACS Style

Vahid Garousi; Görkem Giray; Eray Tüzün; Cagatay Catal; Michael Felderer. Aligning software engineering education with industrial needs: A meta-analysis. Journal of Systems and Software 2019, 156, 65 -83.

AMA Style

Vahid Garousi, Görkem Giray, Eray Tüzün, Cagatay Catal, Michael Felderer. Aligning software engineering education with industrial needs: A meta-analysis. Journal of Systems and Software. 2019; 156 ():65-83.

Chicago/Turabian Style

Vahid Garousi; Görkem Giray; Eray Tüzün; Cagatay Catal; Michael Felderer. 2019. "Aligning software engineering education with industrial needs: A meta-analysis." Journal of Systems and Software 156, no. : 65-83.

Journal article
Published: 30 January 2019 in Computers and Electronics in Agriculture
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Plant species classification is crucial for biodiversity protection and conservation. Manual classification is time-consuming, expensive, and requires experienced experts who are often limited available. To cope with these issues, various machine learning algorithms have been proposed to support the automated classification of plant species. Among these machine learning algorithms, Deep Neural Networks (DNNs) have been applied to different data sets. DNNs have been however often applied in isolation and no effort has been made to reuse and transfer the knowledge of different applications of DNNs. Transfer learning in the context of machine learning implies the usage of the results of multiple applications of DNNs. In this article, the results of the effect of four different transfer learning models for deep neural network-based plant classification is investigated on four public datasets. Our experimental study demonstrates that transfer learning can provide important benefits for automated plant identification and can improve low-performance plant classification models.

ACS Style

Aydin Kaya; Ali Seydi Keceli; Cagatay Catal; Hamdi Yalin Yalic; Huseyin Temucin; Bedir Tekinerdogan. Analysis of transfer learning for deep neural network based plant classification models. Computers and Electronics in Agriculture 2019, 158, 20 -29.

AMA Style

Aydin Kaya, Ali Seydi Keceli, Cagatay Catal, Hamdi Yalin Yalic, Huseyin Temucin, Bedir Tekinerdogan. Analysis of transfer learning for deep neural network based plant classification models. Computers and Electronics in Agriculture. 2019; 158 ():20-29.

Chicago/Turabian Style

Aydin Kaya; Ali Seydi Keceli; Cagatay Catal; Hamdi Yalin Yalic; Huseyin Temucin; Bedir Tekinerdogan. 2019. "Analysis of transfer learning for deep neural network based plant classification models." Computers and Electronics in Agriculture 158, no. : 20-29.

Preprint
Published: 05 December 2018
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According to different reports, many recent software engineering graduates often face difficulties when beginning their professional careers, due to misalignment of the skills learnt in their university education with what is needed in industry. To address that need, many studies have been conducted to align software engineering education with industry needs. To synthesize that body of knowledge, we present in this paper a systematic literature review (SLR) which summarizes the findings of 33 studies in this area. By doing a meta-analysis of all those studies and using data from 12 countries and over 4,000 data points, this study will enable educators and hiring managers to adapt their education / hiring efforts to best prepare the software engineering workforce.

ACS Style

Vahid Garousi; Görkem Giray; Eray Tüzün; Cagatay Catal; Michael Felderer. Closing the gap between software engineering education and industrial needs. 2018, 1 .

AMA Style

Vahid Garousi, Görkem Giray, Eray Tüzün, Cagatay Catal, Michael Felderer. Closing the gap between software engineering education and industrial needs. . 2018; ():1.

Chicago/Turabian Style

Vahid Garousi; Görkem Giray; Eray Tüzün; Cagatay Catal; Michael Felderer. 2018. "Closing the gap between software engineering education and industrial needs." , no. : 1.

Journal article
Published: 01 December 2018 in International Journal of Applied Earth Observation and Geoinformation
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Global burned are algorithms provide valuable information for climate modellers since fire disturbance is responsible of a significant part of the emissions and their related impact on humans. The aim of this work is to explore how four different classification algorithms, widely used in remote sensing, such as Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN) and a well-known decision tree algorithm (C5.0), for classifying burned areas at global scale through a data mining methodology using 2008 MODIS data. A training database consisting of burned and unburned pixels was created from 130 Landsat scenes. The resulting database was highly unbalanced with the burned class representing less than one percent of the total. Therefore, the ability of the algorithms to cope with this problem was evaluated. Attribute selection was performed using three filters to remove potential noise and to reduce the dimensionality of the data: Random Forest, entropy-based filter, and logistic regression. Eight out of fifty-two attributes were selected, most of them related to the temporal difference of the reflectance of the bands. Models were trained using an 80% of the database following a ten-fold approach to reduce possible overfitting and to select the optimum parameters. Finally, the performance of the algorithms was evaluated over six different regions using official statistics where they were available and benchmark burned area products, namely MCD45 (V5.1) and MCD64 (V6). Compared to official statistics, the best agreement was obtained by MCD64 (OE = 0.15, CE = 0.29) followed by RF (OE = 0.27, CE = 0.21). For the remaining three areas (Angola, Sudan and South Africa), RF (OE = 0.47, CE = 0.45) yielded the best results when compared to the reference data. NN and SVM showed the worst performance with omission and commission error reaching 0.81 and 0.17 respectively. SVM and NN showed higher sensitivity to unbalanced datasets, as in the case of burned area, with a clear bias towards the majority class. On the other hand, tree based algorithms are more robust to this issue given their own mechanisms to deal with big and unbalanced databases.

ACS Style

Rubén Ramo; Mariano García; Daniel Rodríguez; Emilio Chuvieco. A data mining approach for global burned area mapping. International Journal of Applied Earth Observation and Geoinformation 2018, 73, 39 -51.

AMA Style

Rubén Ramo, Mariano García, Daniel Rodríguez, Emilio Chuvieco. A data mining approach for global burned area mapping. International Journal of Applied Earth Observation and Geoinformation. 2018; 73 ():39-51.

Chicago/Turabian Style

Rubén Ramo; Mariano García; Daniel Rodríguez; Emilio Chuvieco. 2018. "A data mining approach for global burned area mapping." International Journal of Applied Earth Observation and Geoinformation 73, no. : 39-51.

Journal article
Published: 01 November 2018 in Information Sciences
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Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed version of the CFS algorithm, capable of dealing with the large volumes of data typical of big data applications. Two versions of the algorithm were implemented and compared using the Apache Spark cluster computing model, currently gaining popularity due to its much faster processing times than Hadoop’s MapReduce model. We tested our algorithms on four publicly available datasets, each consisting of a large number of instances and two also consisting of a large number of features. The results show that our algorithms were superior in terms of both time-efficiency and scalability. In leveraging a computer cluster, they were able to handle larger datasets than the non-distributed WEKA version while maintaining the quality of the results, i.e., exactly the same features were returned by our algorithms when compared to the original algorithm available in WEKA.

ACS Style

Raul-Jose Palma-Mendoza; Luis De-Marcos; Daniel Rodriguez; Amparo Alonso-Betanzos. Distributed correlation-based feature selection in spark. Information Sciences 2018, 496, 287 -299.

AMA Style

Raul-Jose Palma-Mendoza, Luis De-Marcos, Daniel Rodriguez, Amparo Alonso-Betanzos. Distributed correlation-based feature selection in spark. Information Sciences. 2018; 496 ():287-299.

Chicago/Turabian Style

Raul-Jose Palma-Mendoza; Luis De-Marcos; Daniel Rodriguez; Amparo Alonso-Betanzos. 2018. "Distributed correlation-based feature selection in spark." Information Sciences 496, no. : 287-299.

Journal article
Published: 01 August 2018 in Decision Support Systems
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Today’s IT systems and IT processes must be ready to handle change in an efficient and responsive manner to allow businesses to both evolve and adapt to a changing world. In this paper we describe an approach that consists of using simulation based multi-objective optimization to select optimal ITIL change management process strategies that help IT managers achieve process efficiency as a Critical Success Factor (CSF). A multi-method simulation model, which is based on agent-based and discrete-event simulation paradigms, has been built to simulate the whole process lifecycle, since the change initiation until its closure. As most engineering problems, assuring an efficient delivery of the change management process requires optimizing simultaneously the corresponding Key Performance Indicators (KPIs) in which the process-efficiency CSF can be rolled down. In this paper, we show the results of applying two well-known Multi-Objective Evolutionary Algorithms, namely NSGA-II and SPEA2, to obtain a set of optimal solutions for the KPIs associated with delivering process efficiency as a CSF. We also compare the results obtained with the output from the single-objective optimization algorithm provided by the simulation tool. The experimental work included shows how the approach can provide the IT manager with a wide range of high quality solutions to support them in their decision-making towards CSF achievement.

ACS Style

Mercedes Ruiz; Javier Moreno; Bernabé Dorronsoro; Daniel Rodriguez. Using simulation-based optimization in the context of IT service management change process. Decision Support Systems 2018, 112, 35 -47.

AMA Style

Mercedes Ruiz, Javier Moreno, Bernabé Dorronsoro, Daniel Rodriguez. Using simulation-based optimization in the context of IT service management change process. Decision Support Systems. 2018; 112 ():35-47.

Chicago/Turabian Style

Mercedes Ruiz; Javier Moreno; Bernabé Dorronsoro; Daniel Rodriguez. 2018. "Using simulation-based optimization in the context of IT service management change process." Decision Support Systems 112, no. : 35-47.

Articles
Published: 12 July 2018 in Behaviour & Information Technology
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Competence-based learning is increasingly widespread in many institutions since it provides flexibility, facilitates the self-learning and brings the academic and professional worlds closer together. Thus, the competence-based recommender systems emerged taking the advantages of competences to offer suggestions (performance of a learning experience, assistance of an expert or recommendation of a learning resource) to the user (learner or instructor). The objective of this work is to conduct a new Systematic Literature Review (SLR) concerning competence-based recommender systems to analyse in relation to their nature and assessment of competences an others key factors that provide more flexible and exhaustive recommendations. To do so, a SLR research methodology was followed in which 25 competence-based recommender systems related to learning or instruction environments were classified according to multiple criteria. We evaluate the role of competences in these proposals and enumerate the emerging challenges. Also a critical analysis of current proposals is carried out to determine their strengths and weakness. Finally, future research paths to be explored are grouped around two main axes closely interlinked; first about the typical challenges related to recommender systems and second, concerning ambitious emerging challenges.

ACS Style

Hector Yago; Julia Clemente; Daniel Rodriguez. Competence-based recommender systems: a systematic literature review. Behaviour & Information Technology 2018, 37, 1 -20.

AMA Style

Hector Yago, Julia Clemente, Daniel Rodriguez. Competence-based recommender systems: a systematic literature review. Behaviour & Information Technology. 2018; 37 (10-11):1-20.

Chicago/Turabian Style

Hector Yago; Julia Clemente; Daniel Rodriguez. 2018. "Competence-based recommender systems: a systematic literature review." Behaviour & Information Technology 37, no. 10-11: 1-20.

Journal article
Published: 01 April 2018 in Computer Methods and Programs in Biomedicine
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It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results.

ACS Style

Cagatay Catal; Akhan Akbulut. Automatic energy expenditure measurement for health science. Computer Methods and Programs in Biomedicine 2018, 157, 31 -37.

AMA Style

Cagatay Catal, Akhan Akbulut. Automatic energy expenditure measurement for health science. Computer Methods and Programs in Biomedicine. 2018; 157 ():31-37.

Chicago/Turabian Style

Cagatay Catal; Akhan Akbulut. 2018. "Automatic energy expenditure measurement for health science." Computer Methods and Programs in Biomedicine 157, no. : 31-37.

Data article
Published: 28 March 2018 in Data in Brief
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Classifying software defects according to any defined taxonomy is not straightforward. In order to be used for automatizing the classification of software defects, two sets of defect reports were collected from public issue tracking systems from two different real domains. Due to the lack of a domain expert, the collected defects were categorized by a set of annotators of unknown reliability according to their impact from IBM's orthogonal defect classification taxonomy. Both datasets are prepared to solve the defect classification problem by means of techniques of the learning from crowds paradigm (Hernández-González et al. [1]). Two versions of both datasets are publicly shared. In the first version, the raw data is given: the text description of defects together with the category assigned by each annotator. In the second version, the text of each defect has been transformed to a descriptive vector using text-mining techniques.

ACS Style

Jerónimo Hernández-González; Daniel Rodriguez; Iñaki Inza; Rachel Harrison; Jose A. Lozano. Two datasets of defect reports labeled by a crowd of annotators of unknown reliability. Data in Brief 2018, 18, 840 -845.

AMA Style

Jerónimo Hernández-González, Daniel Rodriguez, Iñaki Inza, Rachel Harrison, Jose A. Lozano. Two datasets of defect reports labeled by a crowd of annotators of unknown reliability. Data in Brief. 2018; 18 ():840-845.

Chicago/Turabian Style

Jerónimo Hernández-González; Daniel Rodriguez; Iñaki Inza; Rachel Harrison; Jose A. Lozano. 2018. "Two datasets of defect reports labeled by a crowd of annotators of unknown reliability." Data in Brief 18, no. : 840-845.

Regular paper
Published: 22 January 2018 in Knowledge and Information Systems
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Feature selection (FS) is a key research area in the machine learning and data mining fields; removing irrelevant and redundant features usually helps to reduce the effort required to process a dataset while maintaining or even improving the processing algorithm’s accuracy. However, traditional algorithms designed for executing on a single machine lack scalability to deal with the increasing amount of data that have become available in the current Big Data era. ReliefF is one of the most important algorithms successfully implemented in many FS applications. In this paper, we present a completely redesigned distributed version of the popular ReliefF algorithm based on the novel Spark cluster computing model that we have called DiReliefF. The effectiveness of our proposal is tested on four publicly available datasets, all of them with a large number of instances and two of them with also a large number of features. Subsets of these datasets were also used to compare the results to a non-distributed implementation of the algorithm. The results show that the non-distributed implementation is unable to handle such large volumes of data without specialized hardware, while our design can process them in a scalable way with much better processing times and memory usage.

ACS Style

Raul-Jose Palma-Mendoza; Daniel Rodriguez; Luis De-Marcos. Distributed ReliefF-based feature selection in Spark. Knowledge and Information Systems 2018, 57, 1 -20.

AMA Style

Raul-Jose Palma-Mendoza, Daniel Rodriguez, Luis De-Marcos. Distributed ReliefF-based feature selection in Spark. Knowledge and Information Systems. 2018; 57 (1):1-20.

Chicago/Turabian Style

Raul-Jose Palma-Mendoza; Daniel Rodriguez; Luis De-Marcos. 2018. "Distributed ReliefF-based feature selection in Spark." Knowledge and Information Systems 57, no. 1: 1-20.

Journal article
Published: 01 January 2018 in Applied Soft Computing
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ACS Style

Jerónimo Hernández-González; Daniel Rodriguez; Iñaki Inza; Rachel Harrison; Jose A. Lozano. Learning to classify software defects from crowds: A novel approach. Applied Soft Computing 2018, 62, 579 -591.

AMA Style

Jerónimo Hernández-González, Daniel Rodriguez, Iñaki Inza, Rachel Harrison, Jose A. Lozano. Learning to classify software defects from crowds: A novel approach. Applied Soft Computing. 2018; 62 ():579-591.

Chicago/Turabian Style

Jerónimo Hernández-González; Daniel Rodriguez; Iñaki Inza; Rachel Harrison; Jose A. Lozano. 2018. "Learning to classify software defects from crowds: A novel approach." Applied Soft Computing 62, no. : 579-591.

Conference paper
Published: 01 September 2017 in 2017 6th International Conference on Space Mission Challenges for Information Technology (SMC-IT)
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The design of a Compact Dual-band Equatorial helix antenna is presented. These antennas are used for Telemetry, Tracking, and Control (TTC) of satellites from the terrain base station. A simulation-optimization process is presented, a simulation tool named MONURBS is linked with a well-known multi-objective algorithm (NSGA-II) in order to design and optimize the parameters of the antenna. The size of the antenna that fulfills radiation patterns needed for the communication are obtained using simulation together with a multi-objective algorithm. In this work, a comparison with previous designs and the antenna prototype are be presented showing that this approach can achive solutions expediting the process.

ACS Style

Javier Moreno; Ivan Gonzalez; Daniel Rodriguez. Using Simulation and the NSGA-II Evolutionary Multi-Objective Algorithm in the Design of a Compact Dual-Band Equatorial Helix Antenna. 2017 6th International Conference on Space Mission Challenges for Information Technology (SMC-IT) 2017, 56 -60.

AMA Style

Javier Moreno, Ivan Gonzalez, Daniel Rodriguez. Using Simulation and the NSGA-II Evolutionary Multi-Objective Algorithm in the Design of a Compact Dual-Band Equatorial Helix Antenna. 2017 6th International Conference on Space Mission Challenges for Information Technology (SMC-IT). 2017; ():56-60.

Chicago/Turabian Style

Javier Moreno; Ivan Gonzalez; Daniel Rodriguez. 2017. "Using Simulation and the NSGA-II Evolutionary Multi-Objective Algorithm in the Design of a Compact Dual-Band Equatorial Helix Antenna." 2017 6th International Conference on Space Mission Challenges for Information Technology (SMC-IT) , no. : 56-60.

Conference paper
Published: 15 June 2017 in Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering
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Semi-Supervised Learning (SSL) is a data mining technique which comes between supervised and unsupervised techniques, and is useful when a small number of instances in a dataset are labelled but a lot of unlabelled data is also available. This is the case with user reviews in application stores such as the Apple App Store or Google Play, where a vast amount of reviews are available but classifying them into categories such as bug related review or feature request is expensive or at least labor intensive. SSL techniques are well-suited to this problem as classifying reviews not only takes time and effort, but may also be unnecessary. In this work, we analyse SSL techniques to show their viability and their capabilities in a dataset of reviews collected from the App Store for both transductive (predicting existing instance labels during training) and inductive (predicting labels on unseen future data) performance.

ACS Style

Roger Deocadez; Rachel Harrison; Daniel Rodriguez. Preliminary Study on Applying Semi-Supervised Learning to App Store Analysis. Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering 2017, 320 -323.

AMA Style

Roger Deocadez, Rachel Harrison, Daniel Rodriguez. Preliminary Study on Applying Semi-Supervised Learning to App Store Analysis. Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering. 2017; ():320-323.

Chicago/Turabian Style

Roger Deocadez; Rachel Harrison; Daniel Rodriguez. 2017. "Preliminary Study on Applying Semi-Supervised Learning to App Store Analysis." Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering , no. : 320-323.

Journal article
Published: 01 December 2016 in Applied Soft Computing
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Highlights•Definition of a new measure for evaluating estimation models.•The measure is based on the concept of Equivalence Hypothesis Testing.•Application of the measure to estimations by different soft computing methods.•Construction of probability intervals for each estimation method.•Genetic programming and linear regression provide the best intervals. AbstractThis article proposes a new measure to compare soft computing methods for software estimation. This new measure is based on the concepts of Equivalence Hypothesis Testing (EHT). Using the ideas of EHT, a dimensionless measure is defined using the Minimum Interval of Equivalence and a random estimation. The dimensionless nature of the metric allows us to compare methods independently of the data samples used.The motivation of the current proposal comes from the biases that other criteria show when applied to the comparison of software estimation methods. In this work, the level of error for comparing the equivalence of methods is set using EHT. Several soft computing methods are compared, including genetic programming, neural networks, regression and model trees, linear regression (ordinary and least mean squares) and instance-based methods. The experimental work has been performed on several publicly available datasets.Given a dataset and an estimation method we compute the upper point of Minimum Interval of Equivalence, MIEu, on the confidence intervals of the errors. Afterwards, the new measure, MIEratio, is calculated as the relative distance of the MIEu to the random estimation.Finally, the data distributions of the MIEratios are analysed by means of probability intervals, showing the viability of this approach. In this experimental work, it can be observed that there is an advantage for the genetic programming and linear regression methods by comparing the values of the intervals. Graphical abstract

ACS Style

José Javier Dolado; Daniel Rodriguez; Mark Harman; William B. Langdon; Federica Sarro. Evaluation of estimation models using the Minimum Interval of Equivalence. Applied Soft Computing 2016, 49, 956 -967.

AMA Style

José Javier Dolado, Daniel Rodriguez, Mark Harman, William B. Langdon, Federica Sarro. Evaluation of estimation models using the Minimum Interval of Equivalence. Applied Soft Computing. 2016; 49 ():956-967.

Chicago/Turabian Style

José Javier Dolado; Daniel Rodriguez; Mark Harman; William B. Langdon; Federica Sarro. 2016. "Evaluation of estimation models using the Minimum Interval of Equivalence." Applied Soft Computing 49, no. : 956-967.

Conference paper
Published: 01 November 2016 in Proceedings of the 8th International Conference on Digital Arts
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This paper describes the experience of researching and teaching the conceptual and practical basis for the specification, modelling and design of an ontology-based news authoring environment for the Semantic Web, that takes into account the construction and use of an ontology of the Zika disease. Some CMSs are being adapted in order to receive semantic features, such as automatic generations of keywords, semantic annotation and tagging, content reviewing etc. We present here the infrastructure designed to foster research on semantic CMSs as well as semantic web technologies that can be integrated into an ontology-based news authoring environment.

ACS Style

Edgard Costa Oliveira; Edison Ishikawa; Lucas Hiroshi Horinouchi; Thabata Hellen Granja; Marcos V. De A. Nunes; Daniel Rodriguez; Rafael Batista Menegassi; Luciano Gois; George Ghinea. Designing an ontology-based Zika virus news authoring environment for the semantic web. Proceedings of the 8th International Conference on Digital Arts 2016, 197 -203.

AMA Style

Edgard Costa Oliveira, Edison Ishikawa, Lucas Hiroshi Horinouchi, Thabata Hellen Granja, Marcos V. De A. Nunes, Daniel Rodriguez, Rafael Batista Menegassi, Luciano Gois, George Ghinea. Designing an ontology-based Zika virus news authoring environment for the semantic web. Proceedings of the 8th International Conference on Digital Arts. 2016; ():197-203.

Chicago/Turabian Style

Edgard Costa Oliveira; Edison Ishikawa; Lucas Hiroshi Horinouchi; Thabata Hellen Granja; Marcos V. De A. Nunes; Daniel Rodriguez; Rafael Batista Menegassi; Luciano Gois; George Ghinea. 2016. "Designing an ontology-based Zika virus news authoring environment for the semantic web." Proceedings of the 8th International Conference on Digital Arts , no. : 197-203.

Conference paper
Published: 13 September 2016 in Transactions on Petri Nets and Other Models of Concurrency XV
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The loss of motor function in the elderly makes this population group prone to accidental falls. Actually, falls are one of the most notable concerns in elder care. Not surprisingly, there are several technical solutions to detect falls, however, none of them has achieved great acceptance. The popularization of smartwatches provides a promising tool to address this problem. In this work, we present a solution that applies machine learning techniques to process the output of a smartwatch accelerometer, being able to detect a fall event with high accuracy. To this end, we simulated the two most common types of falls in elders, gathering acceleration data from the wrist, then applied that data to train two classifiers. The results show high accuracy and robust classifiers able to detect falls.

ACS Style

Armando Collado Villaverde; Maria R-Moreno; David F. Barrero; Daniel Rodriguez. Triaxial Accelerometer Located on the Wrist for Elderly People’s Fall Detection. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 523 -532.

AMA Style

Armando Collado Villaverde, Maria R-Moreno, David F. Barrero, Daniel Rodriguez. Triaxial Accelerometer Located on the Wrist for Elderly People’s Fall Detection. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():523-532.

Chicago/Turabian Style

Armando Collado Villaverde; Maria R-Moreno; David F. Barrero; Daniel Rodriguez. 2016. "Triaxial Accelerometer Located on the Wrist for Elderly People’s Fall Detection." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 523-532.

Conference paper
Published: 30 August 2015 in Proceedings of the 6th International Workshop on Social Software Engineering
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This work summarizes the main topics that have been researched in the area of software testing under the umbrella of ``Bayesian approaches'' since 2010. There is a growing trend on the use of the so-called Bayesian statistics and Bayesian concepts in general and software testing in particular. Following a Systematic Literature Review protocol using the main digital libraries and repositories, we selected around 40 references applying Bayesian approaches in the field of software testing since 2010. Those references summarise the current state of the art and foster better focused research. So far, the main observed use of the Bayesian concepts in the software testing field is through the application of Bayesian networks for software reliability and defect prediction (the latter is mainly based on static software metrics and Bayesian classifiers). Other areas of application are software estimation and test data generation. There are areas not fully explored beyond the basic Bayesian approaches, such as influence diagrams and dynamic networks.

ACS Style

Daniel Rodriguez; José Javier Dolado; Javier Tuya. Bayesian concepts in software testing: an initial review. Proceedings of the 6th International Workshop on Social Software Engineering 2015, 1 .

AMA Style

Daniel Rodriguez, José Javier Dolado, Javier Tuya. Bayesian concepts in software testing: an initial review. Proceedings of the 6th International Workshop on Social Software Engineering. 2015; ():1.

Chicago/Turabian Style

Daniel Rodriguez; José Javier Dolado; Javier Tuya. 2015. "Bayesian concepts in software testing: an initial review." Proceedings of the 6th International Workshop on Social Software Engineering , no. : 1.